wehub-resource-sync
94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [AUTO-GENERATED] Unit test for paddle.framework.recall_error.check_naninf
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# Target: cover uncovered lines 25-28, 30 in recall_error.py
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import unittest
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import numpy as np
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import paddle
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from paddle.framework.recall_error import (
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LOSS_INF_ERROR,
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LOSS_NAN_ERROR,
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check_naninf,
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)
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class TestCheckNaninf(unittest.TestCase):
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"""Test cases for paddle.framework.recall_error.check_naninf function."""
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def setUp(self):
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paddle.disable_static()
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def test_finite_tensor_returns_none(self):
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"""When all values in the tensor are finite, check_naninf should return None."""
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tensor = paddle.to_tensor([1.0, 2.0, 3.0, -1.0, 0.0])
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result = check_naninf(tensor)
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self.assertIsNone(result)
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def test_nan_tensor_returns_nan_error(self):
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"""When the tensor contains NaN, check_naninf should return LOSS_NAN_ERROR."""
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data = np.array([1.0, float('nan'), 3.0], dtype='float32')
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tensor = paddle.to_tensor(data)
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result = check_naninf(tensor)
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self.assertEqual(result, LOSS_NAN_ERROR)
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def test_inf_tensor_returns_inf_error(self):
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"""When the tensor contains Inf (but no NaN), check_naninf should return LOSS_INF_ERROR."""
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data = np.array([1.0, float('inf'), 3.0], dtype='float32')
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tensor = paddle.to_tensor(data)
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result = check_naninf(tensor)
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self.assertEqual(result, LOSS_INF_ERROR)
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def test_neg_inf_tensor_returns_inf_error(self):
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"""When the tensor contains -Inf, check_naninf should return LOSS_INF_ERROR."""
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data = np.array([1.0, float('-inf'), 3.0], dtype='float32')
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tensor = paddle.to_tensor(data)
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result = check_naninf(tensor)
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self.assertEqual(result, LOSS_INF_ERROR)
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def test_nan_and_inf_tensor_returns_nan_error(self):
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"""When the tensor contains both NaN and Inf, check_naninf should return LOSS_NAN_ERROR
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because NaN check takes priority over Inf (isfinite fails, then isnan is True)."""
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data = np.array([float('nan'), float('inf'), 3.0], dtype='float32')
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tensor = paddle.to_tensor(data)
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result = check_naninf(tensor)
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self.assertEqual(result, LOSS_NAN_ERROR)
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def test_scalar_finite_tensor(self):
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"""Test with a scalar tensor that is finite."""
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tensor = paddle.to_tensor(42.0)
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result = check_naninf(tensor)
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self.assertIsNone(result)
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def test_scalar_nan_tensor(self):
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"""Test with a scalar NaN tensor."""
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data = np.array(float('nan'), dtype='float32')
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tensor = paddle.to_tensor(data)
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result = check_naninf(tensor)
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self.assertEqual(result, LOSS_NAN_ERROR)
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def test_scalar_inf_tensor(self):
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"""Test with a scalar Inf tensor."""
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data = np.array(float('inf'), dtype='float32')
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tensor = paddle.to_tensor(data)
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result = check_naninf(tensor)
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self.assertEqual(result, LOSS_INF_ERROR)
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if __name__ == '__main__':
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unittest.main()
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